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Fibre Bragg grating (FBG) sensors that are immune to electromagnetic interference could advantageously perform cryogenic temperature monitoring in superconducting magnetic fields, but their intrinsic temperature sensitivity is quite poor and must be enhanced. In fact, the low thermal expansion coefficient of silica limits the temperature sensitivity of bare FBG sensors at cryogenic temperature. In this paper the possibility of improving the temperature sensitivity of FBG sensors by metal coating is investigated. Specifically, zinc and copper coating depositions are performed by the traditional electrowinning process, after aluminium pre-coating of the sensor. Coated FBG sensors are inspected by both optical and metallographic techniques. SEM metallographic investigations show that a homogeneous deposit is obtained, with good metal adhesion to the FBG sensor. Optical testing shows that the optical properties of the coated FBG sensors are slightly affected: aluminium pre-coating produces appreciable modification of the diffraction spectrum in both peak width and peak shift, while zinc coating produces a moderate peak shift and copper coating just enlarges the peak width. Results presented in this paper show that both metals appreciably increase the temperature sensitivity of the FBG sensors. Zinc coating provides the highest sensitivity and high-resolution temperature measurements are possible at temperatures as low as 15 K.

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A practical problem in spatial statistics is that of constructing spatial sampling designs for environmental monitoring network. This paper presents a fractal-based criterion for the construction of coverage designs to optimize the location of sampling points. The algorithm does not depend on the covariance structure of the process and provides desirable results for situations in which a poor prior knowledge is available. The statistical characteristics of the method are explored by a simulation study while a design exercise concerning the Pescara area monitoring network is used to demonstrate potential designs under realistic assumptions.